机器学习中如何实时可视化训练/测试数据及生成的图像与语音？

我希望在机器学习训练过程中，实现实时可视化训练与测试数据，同时能实时查看生成的图像、听取生成的语音。下面是我目前已经完成的可视化实现：

现有可视化代码实现 #

1. GAN训练时的样本可视化代码 #

batch_size = 100
epochs = 30
init = tf.global_variables_initializer()
samples = []
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(epochs):
        num_batches = mnist.train.num_examples // batch_size
        for i in range(num_batches):
            batch = mnist.train.next_batch(batch_size)
            batch_images = batch[0].reshape((batch_size, 784))
            batch_images = batch_images * 2 -1
            batch_z = np.random.uniform(-1,1,size=(batch_size, 100))
            _ = sess.run(D_trainer, feed_dict={real_images:batch_images, z:batch_z})
            _ = sess.run(G_trainer, feed_dict={z:batch_z})
        print("ON EPOCH {}".format(epoch))
        sample_z = np.random.uniform(-1,1, size=(1, 100))
        gen_samples = sess.run(generator(z, reuse=True), feed_dict={z:sample_z})
        samples.append(gen_samples)
new_samples = []
#saver = tf.train.Saver(var_list=g_vars)
with tf.Session() as sess:
    #saver.restore(sess,"...")
    for x in range(5):
        sample_z = np.random.uniform(-1,1, size=(1, 100))
        gen_samples = sess.run(generator(z, reuse=True), feed_dict={z:sample_z})
        new_samples.append(gen_samples)
plt.imshow(new_samples[0].reshape(28,28))

2. 情感分析实时折线图可视化代码（需在独立终端运行） #

import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
import time
style.use("ggplot")
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
def animate(i):
    pullData = open("twitter-out.txt","r").read()
    lines = pullData.split('\n')
    xar = []
    yar = []
    x = 0
    y = 0
    for l in lines[-200:]:
        x += 1
        if "pos" in l:
            y += 1
        elif "neg" in l:
            y -= 1
        xar.append(x)
        yar.append(y)
    ax1.clear()
    ax1.plot(xar,yar)
ani = animation.FuncAnimation(fig, animate, interval=1000)
plt.show()

参考目标效果 #

我参考了YouTube视频中1:10:23-1:11:03的实时RNN-LSTM生成效果，希望能实现类似的实时可视化功能。

内容的提问来源于stack exchange，提问作者user6751157